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from SETR.transformer_seg import SETRModel, Vit
import torch 

if __name__ == "__main__":
    net = SETRModel(patch_size=(32, 32), 
                    in_channels=3, 
                    out_channels=1, 
                    hidden_size=1024, 
                    sample_rate=5,
                    num_hidden_layers=1, 
                    num_attention_heads=16, 
                    decode_features=[512, 256, 128, 64])
    t1 = torch.rand(1, 3, 512, 512)
    print("input: " + str(t1.shape))

    print("output: " + str(net(t1).shape))


    model = Vit(patch_size=(32, 32), 
                    in_channels=1, 
                    out_class=10, 
                    sample_rate=4,
                    hidden_size=1024, 
                    num_hidden_layers=1, 
                    num_attention_heads=16)
    
    t1 = torch.rand(1, 1, 512, 512)
    print("input: " + str(t1.shape))

    print("output: " + str(model(t1).shape))

